@inproceedings{18e3f2e20b244df4996bcaee29cc9dec,
title = "Machine learning-based classification of extremism using explainable artificial intelligence",
abstract = "This paper presents the first published application of multiple existing machine learning methods to a subset of features taken from the Profiles of Individual Radicalization in the United States (PIRUS) database to predict the feature {\textquoteleft}violent{\textquoteright}. The best- performing model in terms of accuracy is the Hist Gradient Boosting model, with an accuracy of 89.06%, which is an improvement of more than 2.5% compared to the benchmark application. Permutation Feature Importance (PFI) and the explanation framework SHAP were then applied to explain the model predictions. Using both of these techniques together allows for a holistic view of both the model{\textquoteright}s inner workings and the impact of the features on the results.",
keywords = "Machine Learning, Extremism, PIRUS database, eXplainable AI, SHAP, Permutation Feature Importance",
author = "Anna Rosner and Alexander Gegov and Hopgood, {Adrian Alan} and Odartey Lamptey and Djamila Ouelhadj and {Da Deppo}, Serge",
note = "Publisher Copyright: {\textcopyright} 2024 IEEE.; 12th IEEE International Conference on Intelligent Systems, IS 2024 ; Conference date: 29-08-2024 Through 31-08-2024",
year = "2024",
month = oct,
day = "9",
doi = "10.1109/IS61756.2024.10705241",
language = "English",
isbn = "9798350350999",
series = "International Conference on Intelligent Systems",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
editor = "Vassil Sgurev and Vladimir Jotsov and Vincenzo Piuri and Luybka Doukovska and Radoslav Yoshinov",
booktitle = "2024 IEEE 12th International Conference on Intelligent Systems (IS)",
address = "United States",
}
